A systems biology approach to understanding the energetic balance in sugarcane

Dr. Renato Vicentini

Systems Biology Laboratory

Center for Molecular Biology and Genetic Engineering

State University of Campinas

Workshop on Interdisciplinary Plant Science, FAPESP, December 2013

Biological NetworksScaling Genotype to Phenotype

• Predictive methods capable of scaling from genotype to phenotype can be developing through systems biology coupled with genomics data.

• Three types of biological networks are of major interest in our laboratory.

Class Gene-regulatory network Metabolic network Protein network

Node Genes / transcripts Metabolites Protein species

Edge Induction or repression Biochemical reactionState transition, catalysis

or inhibition

Strategy RNA-seq

In silico kinetic modeling and

Metabolic control analysis

Metabolite Profiling

Enzymes activity

determination and

allosteric regulation

Our Research Goals to Understanding Regulation of Sucrose Metabolism and Storage in Sugarcane

• Elucidate which genes in sugarcane leaves are responsive to changes in the sink:source ratio.

• Investigate the allosteric regulation of key enzymes.

We propose to develop an approach which integrates molecular and systems

biology to investigate these questions in sugarcane.

Why do some sugarcane genotypes accumulate more sucrose in internodes than

others ?

State of the art

• There are evidences that sink tissues exert an influence on the photosynthetic rates and carbohydrate levels of source organs.

• The activity of photosynthesis-related enzymes are modified by the local levels of sugar and hexoses that will be transported to sink.

• As observed in sugarcane, a decreased hexose levels in leaf may act as a signal for increased sink demand, reducing a negative feedback regulation of photosynthesis.

• The signal feedback system indicating sink sufficiency to regulate source activity may be a significant target for manipulation to increase sugarcane sucrose yield.

• Currently, a model that predicts that sucrose accumulation is dependent on a system in which SPS activity exceeds that of acid invertase.

INV Hex

Sink demand

Negative feedback

Allosteric regulation of the SPS enzyme networkPhosphoproteomics approach

Sugarcane extended

night experiment

Schematic representation of the

system that module the rate of

sucrose synthesis by modifications

in the key enzyme SPS.

Sugarcane extended night experimentSucrose metabolism - Circadian regulation

Day Night

Sucrose metabolismCircadian regulation

Manipulation of Sink Capacity

• Nine month-old field-grown plants of two genotypes of Saccharum (L.) spp. contrasting for sucrose accumulation.

• To modify plant source–sink balance, all leaves except leaf +3 were enclosed(simulated effect of internode maturation).

• RNA-seq analysis of control and perturbed system are in progress.

14d* 0d**1d

* Start** End

Sunlight

Enclosed

6d 3d

4 m

6 x 10 m plot

per genotypeUnshaded

leaf +3

Initial ResultsManipulation of Sink Capacity

• The lowest sucrose content genotype (SP83-2847) shows the highest levels of chlorophylls and a highest efficiency in the photosystem II (Fv/Fo), specially in the middle of the day.

Chlorophyll fluorescence parameters (Fv/Fm; Fo/FM; Fv/Fo)

Initial ResultsManipulation of Sink Capacity

Sugarcane de novo assembling transcriptome

De novo assembling workflow. The numbers indicates the amount

of sequences; K, hash-length in base pairs; Dashed arrows, unused

sequences; Gray boxes, comprises the sequences used in the final

transcriptome.

Source-sink differential expressed genes

High sucrose content Low sucrose content

Sink

Source

~1% of transcripts

~5% of transcripts

Gene regulatory network

Results

• More than ten thousand sugarcane coding-genes remain undiscovered (RNA-Seq).

• More than 2,000 ncRNAsconserved between sugarcane and sorghum was revealed.

• ~18% of the conserved

ncRNA presented a

perfect match with at

small RNA.

Cardoso-Silva, CB et al. PLOS One, submitted

Ortologous relationship

Phylexpress

Grasses PoGOs

SugarcanePoGOs

Networks

Carbohydrate biosynthesis

pathways

Gene-regulatory networks

Transcripts, genes and genomes source databases

Sorghum and rice genomes and genes

Transcription assembler of grasses

Angiosperm genomes (arabidopsis, rice,

populus, and sorghum)

Arabidopsis genome

Sugarcane transcripts collection

Microarray andRNA-seq data

Expression normalization and data correlation

Expressions data

Number of sugarcane genes, redundancy in ESTs database (PoGOs) and gene evolution

(dN/dS)

Sugarcane genes overview

SIM4/Blast algorithms

Similarity search

MapMan catalogue annotation

Annotation

Scaling from Genotype to Phenotype

PhosphopeptidesMetabolics Physiological parameters

Vicentini et al 2012. Tropical Plant Biology

Vicentini et al 2012. Tropical Plant Biology

Cardoso-Silva et al 2013. Plos ONE

Sugarcane co-expression network

• Sugarcane meta-network of coexpressed gene clusters generated by HCCA clustering method (85 clusters with 381 edges). Nodes in the meta-network, represent clusters generated by HCCA. Edges between any two nodes represent interconnectivity between the nodes above threshold 0.04.

Sugarcane co-expression network

Regulatory complexes that are conserved in evolution

• By comparing networks from different species it is possible to reduce measurement noise and to reinforce the common signal present in the networks.

• Using the differential expressed genes identified in the source-sink experiments we can detect more than 50% genes inside regulatory complex conserved across sugarcane and rice.

• When Arabidopsis thaliana was included, only two complex still occurring.

Six significant complex were

discovered

Cellulose synthases

Gene Regulatory Network – A Bayesian ApproachThe source-sink experiment

• We detected several gene clusters, including many hubs, that incorporate different regulatory genes (ncRNAs, siRNAs, miRNAs, etc).

Landscape maps sugarcane metanetwork

Young Maturing Mature

Source Sink

decrease

increase

Relative transcriptional

activity

Landscape maps sugarcane metanetworkSpatial evolution

Maturation stageMature plantsSource-sink unbalanced

decrease

increase

Relative

transcriptional

activity

Hive plot of co-expression network with lincRNAs, de novo sorghum and grasses genes

Hive plots panel co-expression network with lincRNAs, de novo sorghum genes and genes inside different taxonomical groups

• Dr. Antonio Figueira

– Dr. Joni Lima

• Dra. Adriana Hemerly

– Flavia

– MSc. Thais

• Dr. Fabio Nogueira

• Dra. Marie-Anne Van Sluys

• Dr. Renato Vicentini

– MSc. Raphael Mattos (miRNAs network, PhD)

– MSc. Natália Murad (Gen2Phe, Phd)

– Msc. Leonardo Alves (Circadian clock, PhD)

– Elton Melo (Phosphoproteomics, Msc)

– Lucas Canesin (lncRNA, Birth/death of genes, Msc)

• Dr. Michel Vincentz

– Dr. Luiz Del Bem

• Dr. Paulo Mazzafera

– Dra. Alexandra Sawaya

– Dra. Paula Nobile

– Dr. Michael dos Santos Brito

– Dr. Igor Cesarino

– Dra. Alexandra Bottcher

– Adriana Brombini dos Santos

• Dra. Anete de Souza

• Dra. Sabrina Chabregas

• Dra. Juliana Felix

• Dr. Marcos Landell

• Dr. Ivan Antônio dos Anjos

• Dra. Silvana Creste

Team and collaborators

We are open to cooperation in thephosphoproteomic/metabolomic analysisand in the enzymatic activity studies.

Supported by: